Senior Machine Learning Engineer

Company: RapidClaims

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Startup

Company Type: Product

Business Model: B2B

Funding Stage: Series A

Industry: Healthcare

Salary Range: ₹ 50-60 Lacs PA

Job Description RapidClaims is a leader in AI-driven revenue cycle management, transforming how US healthcare providers run mid- and end-revenue cycle operations — from medical coding and charge capture through claim scrubbing, denials management, appeals, and payment posting.

The company has raised $11 million in total funding from top investors, including Accel and Together Fund.

Join us as we scale a cloud-native platform that runs self-hosted, fine-tuned Large Language Models, knowledge graphs, and embedding-based retrieval over millions of clinical notes, claims, and payer-policy documents every month. You’ll engineer autonomous pipelines that parse clinical records and translate into codes, provide documentation improvement parameters, and even solve for denials with autonomous calling if needed; Tackle the

deep-domain challenges that make clinical and RCM AI one of the most rewarding problems in tech.

Senior AI/ML Engineer- Job Overview We are hiring a Senior AI/ML Engineer to own the end-to-end applied LLM, retrieval, and evaluation layer of our healthcare AI platform. You will build production systems that automate mid- and end-revenue cycle workflows for US healthcare spanning coding, claim edits, denials triage, appeal generation, and payer-rule reasoning.

This is a production engineering role (not research) focused on building scalable, auditable, and cost-efficient LLM systems in a regulated healthcare environment

What You’ll Own

• Self-Hosted LLM Infrastructure
• Deploy, fine-tune, and operate open-source models (Llama, Qwen, MedGemma, and successors) as our primary inference stack
• Work with vLLM / SGLang / TensorRT-LLM for serving at scale, with disciplined

attention to throughput, tail latency, batching, KV-cache, and GPU economics

• Own fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO) on clinical notes,

claims, and payer-rule data

• Optimize GPU usage, latency, batching, and cost; make build-vs-buy and

hosted-vs-self-hosted trade-offs explicit and measured

• Knowledge Graphs & Embedding-Based Retrieval
• Design and maintain the knowledge graph encoding ICD-10-CM, CPT, HCPCS,

modifiers, HCC, NCCI edits, LCD/NCD policies, and payer-specific rules — and the

relationships between them

• Build embedding-based retrieval over clinical notes, historical claims, denial reasons, and payer-policy corpora — including chunking, embedding model selection, hybrid search, and re ranking
• Combine graph traversal and dense retrieval so every coded line, scrubbed edit, and appeal response is grounded in auditable evidence
• Own ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA,

AMA, NCCI, payer bulletins)

• Evaluation & Monitoring
• Build continuous evaluation pipelines that gate every model, prompt, retrieval, and

graph change before production

• Run offline eval suites grounded in coder- and biller-validated labels; use LLM-as-judge where appropriate, calibrated against human ground truth
• Monitor drift, hallucinations, regressions, and output quality in production; operate

shadow-mode rollouts and per-cohort accuracy tracking (specialty, payer, chart type)

• Track business metrics: chart-level and opportunity-level coding accuracy, denial rate impact, clean-claim rate, cost per chart, and end-to-end latency
• LLM Systems & Prompt Engineering
• Design prompts and context pipelines for coding (CPT, ICD, HCC, E/M), claim edits,

denial classification, and appeal drafting

• Implement structured outputs (JSON, function calling, constrained decoding) on top of the self-hosted stack
• Apply RAG over medical coding standards (CMS, ICD-10, AHA, NCCI) and payer

policies, grounded in the knowledge graph and embedding stores

• Treat prompts as a thin, well-versioned, well-evaluated layer — never the

load-bearing piece

• Agentic Workflows & Tooling — MCP
• Build MCP servers for internal tools: code lookup, NCCI / rule checks, payer logic,

eligibility, denial classification

• Design multi-step agent workflows with audit trails and human-in-the-loop

checkpoints for coder, biller, and AR-analyst review

• Define deterministic vs. LLM-based tool boundaries for reliability — reliability comes

from knowing which is which

What We’re Looking For Must-Have

• 5+ years in ML/AI engineering, including 6+ months in production LLM systems
• Hands-on experience deploying and operating self-hosted LLMs (vLLM, SGLang,

TensorRT-LLM, or equivalent)

• Strong experience designing embedding-based retrieval and/or knowledge graphs for

grounded LLM applications

• Demonstrated ownership of evaluation infrastructure — offline benchmarks, online

monitoring, drift and regression detection

• Strong Python + PyTorch + Hugging Face experience
• Production experience with monitoring, incidents, and system ownership

Strongly Preferred

• Fine-tuning experience (SFT, LoRA, QLoRA, DPO) on domain-specific corpora
• Experience with graph databases (Neo4j, ArangoDB, or equivalent) and graph-aware

retrieval

• Experience with vector databases and hybrid search (BM25 + dense, rerankers)
• Familiarity with LLM observability tools (Langfuse, LangSmith, Arize, Braintrust, or

in-house equivalents)

• Exposure to healthcare, RCM, claims, or other regulated domains
• Experience with MCP or similar tool-orchestration frameworks
• Strong prompt-engineering and LLM-evaluation instincts

What We Offer

• Work on high-impact healthcare AI systems used in real billing and RCM workflows
• Ownership of production LLM, retrieval, and evaluation systems end-to-end
• Solve real-world problems with real constraints (cost, latency, compliance,

auditability)